SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence

SIR: A New Wi eless Senso Ne wo k Rou ing P o ocol Based on A ificial In elligence Julio Ba bancho, Ca los Le´on, Ja ie Molina, and An onio Ba bancho Depa men o Elec onic Technology, Uni e si y o Se ille, C/ Vi gen de A ica, 7. Se ille 41011, Spain Tel: (+034) 954 55 71 92, Fax: (+034) 954 55 28 33 {jba bancho, cleon, jmolina, ayboc}@us.es Abs ac . Cu en ly, Wi eless Senso Ne wo ks (WSNs) a e o med by hund eds o low ene gy and low cos mic o-elec o-mechanical sys ems. Rou ing and low powe consump ion ha e become impo an esea ch is- sues o in e connec his kind o ne wo ks. Howe e , con en ional Quali y o Se ice ou ing models, a e no sui able o ad hoc senso ne wo ks, due o he dynamic na u e o such sys ems. This pape in oduces a new QoS-d i en ou ing algo i hm, named SIR:Senso In elligence Rou ing. We ha e designed an a ificial neu al ne wo k based on Kohonen sel o ganizing ea u es map. E e y node implemen s his a ificial neu al ne wo k o ming a dis ibu ed in elligence and ubiqui ous compu ing sys em. Keywo ds: Wi eless senso ne wo ks (WSN); Ad hoc ne wo ks, Qual- i y o se ice (QoS); Rou ing; A ificial neu al ne wo ks (ANN); Sel - O ganizing Map (SOM). 1 In oduc ion Due o he senso ea u es (low-powe consump ion, low adio ange, low mem- o y, low p ocessing capaci y, and low cos ), sel -o ganizing ne wo k is he bes sui able ne wo k a chi ec u e o suppo applica ions in such a scena io. Goals like efficien ene gy managemen , high eliabili y and a ailabili y, communica ion secu i y, and obus ness ha e become e y impo an issues o be conside ed. Many esea ch cen e s in he whole wo ld ha e ocused hei in es iga ions in his kind o ne wo ks [1]. We p esen in his pape a new ou ing algo i hm which in oduces a ificial in elligence (AI) echniques o measu e he QoS suppo ed by he ne wo k. The wi eless senso ne wo ks (WSN) a chi ec u e as a whole has o ake in o accoun diffe en aspec s, such as he p o ocol a chi ec u e; Quali y-o -Se ice, dependabili y, edundancy and imp ecision in senso eadings; add essing s uc- u es, scalabili y and ene gy equi emen s; geog aphic and da a-cen ic add ess- ing s uc u es; agg ega ing da a echniques; in eg a ion o WSNs in o la ge ne wo ks, b idging diffe en communica ion p o ocols; e c. [2]. 2 SIR: Senso In elligence Rou ing The necessi y o connec i i y among nodes in oduces he ou ing p oblem. In a WSN we need a mul i-hop scheme o a el om a sou ce o a des iny. The p oblem is sol ed by a echnique called ne wo k backbone o ma ion. We p opose a modifica ion on Dijks a’s algo i hm o o m he ne wo k back- bone, wi h he minimum cos pa hs om he base s a ion o oo , , oe e y node in he ne wo k. We ha e named his algo i hm Senso In elligence Rou ing, SIR, which is desc ibed as ollows in able 1. Table 1. SIR algo i hm #1: Se up phase: #3: ∀ i∈T∩Γ( j)calcula e i:= d( j)+wji d( )=0 I i<d( i)dod( i)= i d( i)=  w i i i∈Γ( ) ∞i i/∈Γ( )  #4: I |T|>0go o#2 Γp( i)=  i i∈Γ( ) 0i i/∈Γ( )  I |T|=0s op #2: Find a j∈Tsuch as d( j)=min{d( i)| i∈T} Do T=T−{ j} Once i is designed he backbone o ma ion algo i hm, we ha e o define he way o measu ing he edge weigh pa ame e , wij . We use a QoS defini ion based on h ee ypes o QoS pa ame e s: imeliness, p ecision and accu acy. Due o he dis ibu ed ea u e o senso ne wo ks, ou app oach measu es he QoS le el in a sp ead way, ins ead o an end- o-end pa adigm. Each node es s e e y neighbo link quali y wi h he ansmissions o a specific packe named ping. Wi h hese ansmissions e e y node ob ains mean alues o la ency, e o a e, du y cycle and h oughpu . These a e he ou me ics we ha e define o measu e he ela ed QoS pa ame e s. Once a node has es ed a neighbo link QoS, i calcula es he dis ance o oo using he ob ained QoS alue. The exp ession 1 ep esen s he way a node i calcula es he dis ance o oo h ough node j,whe eqos is a a iable which alue is ob ained as an ou pu o a neu al ne wo k. This ool is desc ibed in sec ion 2.1. d( i)=d( j)·qos (1) 2.1 SOM: Sel O ganizing Map One o he mos powe ul mechanism de eloped in AI is he Sel -O ganizing Map (SOM) model [3], c ea ed by Teu o Kohonen in 1982, a he Uni e si y o Helsinky, Finland. In SOM we can dis inguish wo phases: lea ning phase,andexecu ion phase. SOM gi es an ou pu deno ed by qos. This alue is e u ned by a unc ion Θ defined by he SOM use , acco ding o his aims. Θ depends on he winning neu on: qos =Θ(g). In sec ion 3 we define his unc ion. 3 Pe o mance E alua ion by Simula ion Due o he desi e o e alua e he SIR pe o mance, we ha e c ea ed wo simu- la ion expe imen s unning on ou wi eless senso ne wo k simula o OLIMPO. E e y node in OLIMPO implemen s a neu al ne wo k (online p ocessing). We ha e ocused ou simula ion on a wi eless senso ne wo k composed by 4000 nodes co e ing an a ea o 87 Km2. This is he ypical a ea o a eu opean medium size ci y like Se ille (Spain) o Zu ich (Swi ze land). The densi y o nodes which a e wi hin he ansmission adius o a node es 7. Noise influence o e a node has been modelled as an Addi i e Gaussian Whi e Noise, (AWGN). Noise powe has been modelled as a s ochas ic a iable wi h a mean alue exp essed as a pe cen age o he an enna sensibili y; and a s anda d de ia ion exp essed as a pe cen age o i s mean alue. Ou SOM has a fi s laye o med by ou inpu neu ons, co esponding wi h e e y me ic (la ency, h oughpu , e o a e and du y cycle); and a second laye o med by wel e ou pu neu ons o ming a 3x4 ma ix. Nex , we de ail ou SOM implemen a ion p ocess. Lea ning Phase. In o de o o ganize he neu ons in a wo dimensional map, we need a se o inpu samples x( )=[la ency( ), h oughpu ( ),e o - a e( ),du y- cycle( )]. This samples should conside all he QoS en i onmen s in which a link communica ion be ween a pai o senso nodes can wo k. Fo ha eason we ha e o c ea e he special en i onmen s. These scena ios a e implemen ed by diffe en noise simula ions. In ou esea ch we c ea ed a WSN o e OLIMPO composed by 4000 senso nodes. In his ne wo k, we chose a pai o nodes (le us deno e hem as 800 and 1250) and in oduced a low powe noise in o one o hem (e.g. 1250). Acco ding o he inpu equi emen s, we had o measu e he QoS me ics. In ha sense, we an a ping applica ion 50 imes a node 800. This applica ion pings a e sen om node 800 o node 1250. Ping equi es acknowledgmen (ACK). The way node 800 ecei es ACKs will de e mine a specific QoS en i onmen , exp essed on he ou elec ed me ics: la ency (seconds), h oughpu (bi s/sec), e o a e (%) and du y cycle(%) [4]. This p ocess was epea ed 100 imes while inc easing he noise powe . Wi h he se o 100 inpu samples we ained ou neu al ne wo k. This p ocess was implemen ed on a pe sonal compu e using he MATLABneu al oolbox (offline p ocessing). Once we had o de ed he neu ons on he Kohonen laye , we iden ified each one o he se o 100 inpu samples wi h an ou pu laye neu on. Acco ding o his p ocedu e he se o 100 inpu samples we e dis ibu ed o e he SOM. We ealized ha inpu samples ob ained om a simila noisy en i onmen and wi h simila QoS ea u es we e alloca ed in a specific egion o he SOM. Consequen ly we ob ained a map o med by clus e s, whe e e e y clus e co esponded wi h a noise le el in oduced a he en i onmen and consequen ly a specific QoS. Fu he mo e, a synap ic-weigh ma ix is o med, whe e e e y synapsis iden ifies a connec ion be ween inpu and ou pu laye . In o de o quan i y he QoS le el we s udied e e y clus e ea u es and as- signed a alue be ween 0.2 and 10, acco ding o he le el o noise in oduced. This assignmen was based on ou expe ience as expe s in ne wo ks. In ha way we defined he ou pu unc ion Θ(i, j),i∈[1,3],j ∈[1,4] wi h wel e alues co esponding wi h e e y neu on (i, j),i∈[1,3],j ∈[1,4]. Execu ion Phase. E e y senso node measu es he QoS o i s links collec ing inpu samples and unning he wining neu on elec ion algo i hm. Fo example, i a specific inpu sample is qui e simila han he synap ic-weigh - ec o o neu on (2,2), his neu on will be ac i a ed. A e he winning neu on is elec ed, he node uses he ou pu unc ion Θ o assign a QoS es ima ion, qos. Finally his alue is employed o modified he dis ance o oo (eq. 1). Ou SIR algo i hm has been e alua ed by he ealiza ion o wo expe imen s de ailed as ollows. Expe imen #1. Fi s , a wi eless senso ne wo k wi h 4000 nodes is c ea ed. The ne wo k backbone is o med using SIR algo i hm, as de ailed in able 2. Howe e , no SOM is applied, so, he dis ance om a node i o oo d( i)isno modified by he neighbo link quali y, qos (eq.1). We ha e called his algo i hm as No AI algo i hm. Nex , a high le el o noise is in oduced a nodes 100 and 200,figu e1.c. Finally, a specific node (e.g. node numbe 300) uns he ‘T ansmi clock o base s a ion’ applica ion. Node 300 sends 10 packe s o oo o measu e he la ency. E e y packe con ains ‘clock’ in o ma ion. (a) (b) noisy s a ions noisy s a ions 0 20 40 60 80 100 120 140 160 180 200 0 5 10 15 20 25 30 Mean noise (% Ps) (when noise s anda d de ia ion is se o 50% o Mean Noise) Clock la ency (sec) SIR ou ing algo i hm NoAI ou ing algo i hm (c) (d) 0 50 100 150 200 0 50 100 0 5 10 15 20 25 30 Noise s anda d de ia ion (% Mean noise) Mean noise (% Ps) Clock la ency (sec) Fig. 1. (a) Clock la ency measu emen : No AI pe o mance (b) Clock la ency measu e- men : SIR e sus No AI (c) Ne wo k backbone o ma ion based on No AI algo i hm (d) Ne wo k backbone o ma ion based on SIR algo i hm. Figu e 1.a ep esen s clock la ency depending on he le el o he noise powe in oduced a nodes 100 and 200. Expe imen #2. This expe imen is simila han expe imen #1, bu in his case, he dis ance d( i) is modified by he neighbo link quali y, using equa ion 1. The ne wo k backbone is o med in such a way ha he pa h c ea ed om he node 300 o oo does no con ain he noisy nodes, figu e 1.d. Figu e 1.b shows SIR algo i hm pe o mance compa ed wi h No AI algo i hm pe o mance. As depic ed in his figu e, i he mean noise is low, bo h algo i hm pe o mances a e excellen . In his case, he pa h om he node 300 o oo con ains nodes 100 and 200. Howe e , when he mean noise g ows up abo e he an enna sensibili y SIR algo i hm pe o mance imp o es No AI algo i hm pe o mance, main aining he QoS. In his case, he pa h om he node 300 o oo does no con ain he noisy nodes. 4 Conclusion and Fu u e Wo ks SIR has been p esen ed in his pape as an inno a i e QoS-d i en ou ing algo- i hm based on a ificial in elligence. This ou ing p o ocol can be used o e wi e- less senso ne wo ks s anda d p o ocols, such as IEEE 802.15.4 and Blue oo h, and o e o he well known p o ocols such as A achne,SMACS,EAR,LEACH, e c. The inclusion o AI echniques (e.g. neu al ne wo ks) in wi eless senso ne - wo ks has been p o ed o be an use ul ool o imp o e ne wo k pe o mances. An addi ional ad an age is he low cos he AI implemen a ion ep esen s. The g ea effo made o implemen a SOM algo i hm inside a senso node means ha he use o a ificial in elligence echniques can imp o e he WSN pe - o mance. Acco ding o his idea, we a e wo king on he design o new p o ocols using his kind o ools. Re e ences 1. I.F. Akyildiz, Y. Su, W. Sanka asub amaniam, and E. C¸ayi ci. Wi eless senso ne wo ks: A su ey. Compu e Ne wo ks, Else ie , 38:393–422, Decembe 2002. 2. F.J. Molina, J. Ba bancho, and J. Luque. Au oma ed me e eading and SCADA applica ion. Lec u e No es in Compu e Science, Sp inge Ve lag, 2865:223–234, Oc obe 2003. 3. T. Kohonen. The sel -o ganizing map. In P occedings o he IEEE, olume 78, pages 1464–1480, 1990. 4. R. Iye and L. Klein ock. QoS con ol o senso ne wo ks. In IEEE In e na ional Con e ence on Communica ions, ICC’03, olume 1, pages 517–521. IEEE P ess, May 2003.